<?xml version="1.0" encoding="UTF-8"?><doi_batch xmlns="http://www.crossref.org/grant_id/0.2.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.crossref.org/grant_id/0.2.0 https://gitlab.com/crossref/schema/-/raw/master/schemas/grant_id0.2.0.xsd" version="0.2.0">  <head>    <doi_batch_id>batch_auto_20260713</doi_batch_id>    <timestamp>202607131256465730</timestamp>    <depositor>      <depositor_name>BOEM ESP geoESPIS</depositor_name>      <email_address>ESPIS@boem.gov</email_address>    </depositor>    <registrant>Bureau of Ocean Energy Management</registrant>  </head>  <body>    <grant>      <project>        <project-title xml:lang="en">Data Synthesis and High-resolution Predictive Modeling of Marine Bird Spatial Distributions on the Pacific OCS</project-title>          <investigators>          <person role="investigator">            <alternateName>Western Ecological Research Center</alternateName>            <affiliation>              <institution country="US">United States Geological Survey</institution>            </affiliation>          </person>          </investigators>        <description xml:lang="en">This study developed seasonal habitat-based spatial models of the at-sea distribution for 33 individual species and 13 taxonomic groups of marine birds throughout the Pacific OCS region. A statistical modeling framework was used to estimate relationships between bird sighting data and a range of temporal (e.g., Pacific Decadal Oscillation index), spatially static (e.g., depth, slope), and spatially dynamic (e.g., sea surface chlorophyll concentration and temperature, current velocity, wind stress) environmental variables. The estimated relationships were then used to predict the long-term spatial distribution of each species throughout the study region in each season. Bird sighting data came from multiple scientific survey programs and consisted of at-sea counts of birds collected between 1980 and 2017 using boat-based and fixed-wing aerial transect survey methods. Spatial environmental variables were derived from remote sensing satellite data and an ocean dynamics model. Results of this study will inform spatial planning and risk assessment in the region.</description>        <award_amount currency="USD">89910</award_amount>        <funding amount="89910" currency="USD" funding-percentage="100" funding-type="award">          <funder-name>U.S. Department of the Interior, Bureau of Ocean Energy Management, Environmental Studies Program</funder-name>          <funder-id>https://doi.org/10.13039/100012475</funder-id>        </funding>        <award-dates start-date="2016-06-18" end-date="2019-09-03"/>      </project>      <award-number>M15PG00009</award-number>      <doi_data>        <doi>10.67297/9A1DA0</doi>        <resource>https://boem.gov/awardpages/100128</resource>      </doi_data>    </grant>  </body></doi_batch>